Developing an approach to evaluate stocks by forecasting effective features with data mining methods

Barak, Sasan; Modarres, Mohammad

doi:10.1016/j.eswa.2014.09.026

Cited by 81 publications

(42 citation statements)

References 45 publications

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“…As can be seen, the number of rules is decreased regarding hybrid pattern which shows stationarity of the model and less over training and over fitting. According to [59], the dense structure of models brings about a low level of over fitting and causes a robust prediction.…”

Section: Arima-anfis Patternsmentioning

confidence: 99%

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Barak

Sadegh

2016

International Journal of Electrical Power & Energy Systems

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269

113

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a b s t r a c tEnergy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA-ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented.The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done. IntroductionEnergy is vital important for development of every country from the social, economic and environmental perspective. It has magnificent effect on industrial and agricultural products, health, sanitary, population, education and human life quality [1].As energy is a crucial input to industrial part of country, energy demand increases along the industrial function increase. Rapid changes in industry and economy strongly affect energy consumption. Therefore, energy consumption is an important economical index that represents economic development of a city or a country [2]. According to the international energy agent report, there should be many transformations in amount and type of future energy consumption (year 2030). As over the past decade global energy consumption has increased rapidly because of population and economic growth [3,4]. According to wide growth of energy consumption in the last decade, energy demand management is very important for achieving economic success, environment preservation and suitable planning for existing resources that result in self-sufficiency and economic development. Therefore, various techniques have been used for energy demand management to forecast future energy demands accurately [4]. However, energy forecasting is difficult, because it is affected by rapid development of economy, technology, gove...

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Section: Arima-anfis Patternsmentioning

confidence: 99%

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Barak

Sadegh

2016

International Journal of Electrical Power & Energy Systems

Self Cite

269

113

View full text Add to dashboard Cite

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“…Number of studies have claimed and verified that feature selection (FS) is the key process in stock prediction (Barak & Modarres, 2015;Tsai & Hsiao, 2010). Feature selection decreases the calculation cost by decreasing the corresponding dimensionality, or improves the forecasting performance by elimination of extra and unrelated features (Crone & Kourentzes, 2010).…”

Section: Introductionmentioning

confidence: 99%

Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

Barak

Dahooie

Tichý

2015

Expert Systems with Applications

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Please cite this article as: Sasan Barak , Tomáš Tichý , Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick, Expert Systems With Applications (2015), AbstractPredicting stock prices is an important objective in the financial world. This paper presents a novel forecasting model for stock markets on the basis of the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) -ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick. Two approaches of Raw-based and Signal-based are devised to extract the model's input variables with 15 and 24 features, respectively. The correct predictions percentages for periods of 1-6 days with the total number of buy and sell signals are considered as output variables. . In proposed model, the ANFIS prediction results are used as a cost function of wrapper model and ICA is used to select the most appropriate features. This novel combination of feature selection not only takes advantage of ICA optimization swiftness, but also the ANFIS prediction accuracy. The emitted buy and sell signals of the model revealed that Signal databases approach gets better results with %87 prediction accuracy and the wrapper features selection obtains 12 % improvement in predictive performance regarding to the base study. Additionally, since the wrapper-based feature selection models are considerably more time-consuming, our presented wrapper ANFIS-ICA algorithm's results have superiority in time decreasing as well as prediction accuracy increasing regarding to other algorithms such as wrapper Genetic algorithm (GA).

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“…Twelve of the thirty papers have used price earnings ratio, earnings per share, Net Asset Value, General Index (GI), share volume, price per annum, book value, face value, financial status of company, stock buy/sell news, dividend yield, treasury bill rate, current ratio, financial leverage ratio, income statement, revenue growth, Growth in net sales, Growth in net profit, Return on Equity, Net Profit Margin (NPM), Price/Sales ratio etc. as their main attributes to predict share market performance [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The main purpose of using these variables is because it deals with the value of the company stock with regards to its potential growth in future earnings.…”

Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur³

2016

IJECE

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Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.

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Developing an approach to evaluate stocks by forecasting effective features with data mining methods

Cited by 81 publications

References 45 publications

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

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